| Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution |
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| 作者姓名： | 周仲兴 王大海 程龙龙 李轶 明东 张力新 万柏坤 |
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| 作者单位： | School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，School of Precision Instrument and Opto-Electronics Engineering Tianjin University，Tianjin 300072 China，Tianjin 300072 China，Tianjin 300072 China，Tianjin 300072 China，Tianjin 300072 China，Tianjin 300072 China，Tianjin 300072 China |
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| 摘 要： | Feature extraction of electrocardiogram(ECG) is oneof the mostimportanttasksin heart disease diagnosis .Gen-erally,these algorithms include length and energytransfor-mation1],hidden Markov models2],neural networks3],and wavelet transform4], etc . Howe…
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| 关 键 词： | 心电图 子波区域 数学 形态学 |
| 修稿时间： | 2007-04-11 |
Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution |
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| ZHOU Zhongxing,WANG Dahai,CHENG Longlong,LI Yi,MING Dong,ZHANG Lixin,WAN Baikun.Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution[J].Transactions of Tianjin University,2007,13(4):235-241. |
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| Authors: | ZHOU Zhongxing WANG Dahai CHENG Longlong LI Yi MING Dong ZHANG Lixin WAN Baikun |
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| Institution: | School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China |
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| Abstract: | In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average). |
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| Keywords: | electrocardiogram (ECG) mathematical morphology wavelet domain evolving Lorentz differential deconvolution |
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